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Local Shearlet Energy Gammodian Pattern (LSEGP): A Scale Space Binary Shape Descriptor for Texture Classification

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Intelligence Enabled Research

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1109))

Abstract

A novel texture feature is proposed in this paper to classify texture images. To represent the local texture feature in different scales and spaces, a novel local gammodian binary pattern (LGBP) is applied on the shearlet transform domain. The main advantage of the proposed gammodian structure is the size of the feature vector. Again, the LGBP is also very effective in capturing local edge information. Finally, the local shearlet energy gammodian pattern (LSEGP) is proposed. The output result of the proposed LSEGP on Outex database shows the effectiveness of the proposed descriptor in texture classification.

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Authors and Affiliations

  1. RCC Institute of Information Technology, Canal South Road, Beliaghata, Kolkata, 700015, India

    Priya Sen Purkait & Hiranmoy Roy

  2. Jadavpur University, Kolkata, 700032, India

    Debotosh Bhattacharjee

Authors
  1. Priya Sen Purkait
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  2. Hiranmoy Roy
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  3. Debotosh Bhattacharjee
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Corresponding author

Correspondence to Hiranmoy Roy .

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Editors and Affiliations

  1. RCC Institute of Information Technology, Kolkata, India

    Siddhartha Bhattacharyya

  2. Indian Statistical Institute, Kolkata, India

    Sushmita Mitra

  3. Visva-Bharati University, Santiniketan, India

    Paramartha Dutta

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Purkait, P.S., Roy, H., Bhattacharjee, D. (2020). Local Shearlet Energy Gammodian Pattern (LSEGP): A Scale Space Binary Shape Descriptor for Texture Classification. In: Bhattacharyya, S., Mitra, S., Dutta, P. (eds) Intelligence Enabled Research. Advances in Intelligent Systems and Computing, vol 1109. Springer, Singapore. https://doi.org/10.1007/978-981-15-2021-1_14

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